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- from numbers import Number
- import torch
- from torch._six import nan
- from torch.distributions import constraints
- from torch.distributions.distribution import Distribution
- from torch.distributions.utils import broadcast_all
- class Uniform(Distribution):
- r"""
- Generates uniformly distributed random samples from the half-open interval
- ``[low, high)``.
- Example::
- >>> m = Uniform(torch.tensor([0.0]), torch.tensor([5.0]))
- >>> m.sample() # uniformly distributed in the range [0.0, 5.0)
- tensor([ 2.3418])
- Args:
- low (float or Tensor): lower range (inclusive).
- high (float or Tensor): upper range (exclusive).
- """
- # TODO allow (loc,scale) parameterization to allow independent constraints.
- arg_constraints = {'low': constraints.dependent(is_discrete=False, event_dim=0),
- 'high': constraints.dependent(is_discrete=False, event_dim=0)}
- has_rsample = True
- @property
- def mean(self):
- return (self.high + self.low) / 2
- @property
- def mode(self):
- return nan * self.high
- @property
- def stddev(self):
- return (self.high - self.low) / 12**0.5
- @property
- def variance(self):
- return (self.high - self.low).pow(2) / 12
- def __init__(self, low, high, validate_args=None):
- self.low, self.high = broadcast_all(low, high)
- if isinstance(low, Number) and isinstance(high, Number):
- batch_shape = torch.Size()
- else:
- batch_shape = self.low.size()
- super(Uniform, self).__init__(batch_shape, validate_args=validate_args)
- if self._validate_args and not torch.lt(self.low, self.high).all():
- raise ValueError("Uniform is not defined when low>= high")
- def expand(self, batch_shape, _instance=None):
- new = self._get_checked_instance(Uniform, _instance)
- batch_shape = torch.Size(batch_shape)
- new.low = self.low.expand(batch_shape)
- new.high = self.high.expand(batch_shape)
- super(Uniform, new).__init__(batch_shape, validate_args=False)
- new._validate_args = self._validate_args
- return new
- @constraints.dependent_property(is_discrete=False, event_dim=0)
- def support(self):
- return constraints.interval(self.low, self.high)
- def rsample(self, sample_shape=torch.Size()):
- shape = self._extended_shape(sample_shape)
- rand = torch.rand(shape, dtype=self.low.dtype, device=self.low.device)
- return self.low + rand * (self.high - self.low)
- def log_prob(self, value):
- if self._validate_args:
- self._validate_sample(value)
- lb = self.low.le(value).type_as(self.low)
- ub = self.high.gt(value).type_as(self.low)
- return torch.log(lb.mul(ub)) - torch.log(self.high - self.low)
- def cdf(self, value):
- if self._validate_args:
- self._validate_sample(value)
- result = (value - self.low) / (self.high - self.low)
- return result.clamp(min=0, max=1)
- def icdf(self, value):
- result = value * (self.high - self.low) + self.low
- return result
- def entropy(self):
- return torch.log(self.high - self.low)
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